knitr::opts_chunk$set(echo = FALSE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
library(SingleR)
library(dplyr)
library(tidyr)
library(limma)
library(scRNAseq)
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] scRNAseq_2.2.0              limma_3.44.3               
##  [3] tidyr_1.1.1                 dplyr_1.0.2                
##  [5] SingleR_1.2.4               MAST_1.14.0                
##  [7] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.2
##  [9] DelayedArray_0.14.1         matrixStats_0.56.0         
## [11] Biobase_2.48.0              GenomicRanges_1.40.0       
## [13] GenomeInfoDb_1.24.2         IRanges_2.22.2             
## [15] S4Vectors_0.26.1            BiocGenerics_0.34.0        
## [17] data.table_1.13.0           ggplot2_3.3.2              
## [19] Seurat_3.2.0               
## 
## loaded via a namespace (and not attached):
##   [1] AnnotationHub_2.20.1          BiocFileCache_1.12.1         
##   [3] plyr_1.8.6                    igraph_1.2.5                 
##   [5] lazyeval_0.2.2                splines_4.0.2                
##   [7] BiocParallel_1.22.0           listenv_0.8.0                
##   [9] digest_0.6.25                 htmltools_0.5.0              
##  [11] magrittr_1.5                  memoise_1.1.0                
##  [13] tensor_1.5                    cluster_2.1.0                
##  [15] ROCR_1.0-11                   globals_0.12.5               
##  [17] colorspace_1.4-1              blob_1.2.1                   
##  [19] rappdirs_0.3.1                ggrepel_0.8.2                
##  [21] xfun_0.16                     crayon_1.3.4                 
##  [23] RCurl_1.98-1.2                jsonlite_1.7.0               
##  [25] spatstat_1.64-1               spatstat.data_1.4-3          
##  [27] survival_3.2-3                zoo_1.8-8                    
##  [29] ape_5.4-1                     glue_1.4.1                   
##  [31] polyclip_1.10-0               gtable_0.3.0                 
##  [33] zlibbioc_1.34.0               XVector_0.28.0               
##  [35] leiden_0.3.3                  BiocSingular_1.4.0           
##  [37] future.apply_1.6.0            abind_1.4-5                  
##  [39] scales_1.1.1                  DBI_1.1.0                    
##  [41] miniUI_0.1.1.1                Rcpp_1.0.5                   
##  [43] viridisLite_0.3.0             xtable_1.8-4                 
##  [45] reticulate_1.16               bit_4.0.4                    
##  [47] rsvd_1.0.3                    htmlwidgets_1.5.1            
##  [49] httr_1.4.2                    RColorBrewer_1.1-2           
##  [51] ellipsis_0.3.1                ica_1.0-2                    
##  [53] pkgconfig_2.0.3               uwot_0.1.8                   
##  [55] dbplyr_1.4.4                  deldir_0.1-28                
##  [57] tidyselect_1.1.0              rlang_0.4.7                  
##  [59] reshape2_1.4.4                later_1.1.0.1                
##  [61] AnnotationDbi_1.50.3          munsell_0.5.0                
##  [63] BiocVersion_3.11.1            tools_4.0.2                  
##  [65] generics_0.0.2                RSQLite_2.2.0                
##  [67] ExperimentHub_1.14.1          ggridges_0.5.2               
##  [69] evaluate_0.14                 stringr_1.4.0                
##  [71] fastmap_1.0.1                 yaml_2.2.1                   
##  [73] goftest_1.2-2                 knitr_1.29                   
##  [75] bit64_4.0.2                   fitdistrplus_1.1-1           
##  [77] purrr_0.3.4                   RANN_2.6.1                   
##  [79] pbapply_1.4-3                 future_1.18.0                
##  [81] nlme_3.1-148                  mime_0.9                     
##  [83] compiler_4.0.2                plotly_4.9.2.1               
##  [85] curl_4.3                      png_0.1-7                    
##  [87] interactiveDisplayBase_1.26.3 spatstat.utils_1.17-0        
##  [89] tibble_3.0.3                  stringi_1.4.6                
##  [91] lattice_0.20-41               Matrix_1.2-18                
##  [93] vctrs_0.3.2                   pillar_1.4.6                 
##  [95] lifecycle_0.2.0               BiocManager_1.30.10          
##  [97] lmtest_0.9-37                 RcppAnnoy_0.0.16             
##  [99] BiocNeighbors_1.6.0           cowplot_1.0.0                
## [101] bitops_1.0-6                  irlba_2.3.3                  
## [103] httpuv_1.5.4                  patchwork_1.0.1              
## [105] R6_2.4.1                      promises_1.1.1               
## [107] KernSmooth_2.23-17            gridExtra_2.3                
## [109] codetools_0.2-16              MASS_7.3-52                  
## [111] assertthat_0.2.1              withr_2.2.0                  
## [113] sctransform_0.2.1             GenomeInfoDbData_1.2.3       
## [115] mgcv_1.8-31                   grid_4.0.2                   
## [117] rpart_4.1-15                  rmarkdown_2.3                
## [119] DelayedMatrixStats_1.10.1     Rtsne_0.15                   
## [121] shiny_1.5.0
## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.

Introduction

In v2 of the analysis we decided to include the control mice from the Nbeal experiment with the Migr1 and Mpl mice. The thought is that it may be good to have another control, since the Migr1 control has irradiated and had a bone marrow transplantation. I’m going to split the Rmarkdown files into separate part, to better organize my analysis.

This File

I’m going to go with the consensus names from the labeling stage and produce figures covering the distribution of cell types within clusters, conditions (enriched/not enriched), experiments (Mpl, Migr, Nbeal_cnt), states(condition + experiment), etc.

##    Cluster SingleR.comb SingleR.ref1 SingleR.ref2 SingleR_cell_comb
## 1        0  Neutrophils  Neutrophils Granulocytes        Neutrophil
## 2        1 Granulocytes  Neutrophils Granulocytes      Granulocytes
## 3        2   Stem cells   Stem Cells Granulocytes        Stem Cells
## 4        3      B cells      B cells      B cells            B cell
## 5        4  Neutrophils  Neutrophils Granulocytes        Neutrophil
## 6        5    Monocytes    Monocytes    Monocytes          Monocyte
## 7        6    Basophils    Basophils Granulocytes          Basophil
## 8        7   Stem cells   Stem Cells    Monocytes        Stem cells
## 9        8  Macrophages  Macrophages  Macrophages       Macrophages
## 10       9      B cells      B cells      B cells           B cells
## 11      10 Erythrocytes      B cells Erythrocytes      Erythrocytes
## 12      11      T cells      T cells      T cells           T cells
## 13      12   Stem cells   Stem Cells Erythrocytes        Stem Cells
## 14      13      B cells      B cells      B cells           B cells
## 15      14         <NA>      B cells      B cells              <NA>
##                    markers      hum_ref         final        final2
## 1              Granulocyte                Granulocyte   Granulocyte
## 2                     <NA>                Granulocyte   Granulocyte
## 3              Granulocyte Erythrocytes    Stem Cells          ?GMP
## 4                  B-cells      B cells        B cell        B cell
## 5                     <NA>                Granulocyte   Granulocyte
## 6                Monocytes    Dendritic      Monocyte      Monocyte
## 7            Mast Cell/MEP                  ?MEP/Mast     ?MEP/Mast
## 8             Granulocyte?        Prog.         ?Prog   ?CMP/Neutro
## 9      Monocyte/Macrophage    Monocytes    Macrophage    Macrophage
## 10                 B-cells      B cells        B cell        B cell
## 11             Erythrocyte  Erythrocyte   Erythrocyte   Erythrocyte
## 12                 T-cells      T cells        T cell        T cell
## 13           Megakaryocyte    HSPCs/Ery Megakaryocyte Megakaryocyte
## 14 Lymphocyte/Stromal Cell                     B cell        B cell
## 15             Plasma Cell       Plasma        B cell        B cell

UMAP Projections

UMAP projections of the data of different subsets of the data with the cell type labels.

Quantification (Bar graphs & Tables/Heatmaps)